Google Generative AI Leader Exam Syllabus
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Google Generative AI Leader Exam Objectives
| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of gen AI | 30% | Dening core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models). Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement). Identifying the stages of the machine learning lifecycle: data ingestion, data preparation, model training, model deployment, and model management; and the Google Cloud tools for each stage. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost, performance, tuning, and customization). Identifying business use cases where gen AI can create, summarize, discover, and automate (e.g., text generation, image generation, code generation, video generation, data analysis, and personalized user experience). Describing how various data types are used in gen AI and the business implications. ? Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format). Identifying the dierences between structured and unstructured data, and identifying real-world examples of each type. Identifying the dierences between labeled and unlabeled data 1.2 Describe how various data types are used in gen AI and the business implications. Considerations include: Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format). Identifying the dierences between structured and unstructured data, and identifying real-world examples of each type. Identifying the dierences between labeled and unlabeled data. 1.3 Identify the core layers of the gen AI landscape and the business implications. Considerations include: Infrastructure Models Plaorms Agents Applications 1.4 Identify the use cases and strengths of Google’s foundation models. Considerations include: ? Gemini ? Gemma ? Imagen |
| Google Cloud’s gen AI oerings | 35% | 2.1 Describe Google Cloud's strengths in the eld of gen AI. Considerations include: Describing how Google's AI-rst approach and commitment to future innovation translate into cuing-edge gen AI solutions. Describing how Google Cloud has an enterprise-ready AI plaorm (e.g., responsible, secure, private, reliable, scalable). Recognizing the advantages of Google's comprehensive AI ecosystem (e.g., integration of gen AI across Google products and services). Describing the benets of Google Cloud's open approach. Identifying the essential components of Google Cloud’s AI-optimized infrastructure and its benets (e.g., hypercomputer, Google’s custom-designed TPUs, GPUs, data centers, cloud computing). Explaining how Google Cloud's AI plaorm provides users with control over their data (e.g., security, privacy, governance, open and leading rst party models, pre-built and customizable solutions, agents). ? Describing how Google Cloud's AI plaorm democratizes AI development (e.g., low-code and no-code tools, pre-trained models, APIs). 2.2 Describe how Google Cloud’s prebuilt gen AI oerings enable AI powered work. Considerations include: Recognizing the functionality, use cases, and business value of the Gemini app and Gemini Advanced (e.g., Gems). Recognizing the functionality, use cases, and business value of Google Agentspace (e.g., Cloud NotebookLM API, multimodal search, and custom agent capabilities). Recognizing the functionality, use cases, and business value of Gemini for Google Workspace. 2.3 Describe how Google Cloud’s gen AI oerings improve the customer experience. Considerations include: Recognizing the functionality, use cases, and business benets of Google Cloud’s external search oerings (e.g., Vertex AI Search, Google Search). Recognizing the functionality, use cases, and business value of Google’s Customer Engagement Suite (e.g., Conversational Agents, Agent Assist, Conversational Insights, Google Cloud Contact Center as a Service). 2.4 Describe how Google Cloud empowers developers to build with AI. Considerations include: Recognizing the functionality, use cases, and business value of Vertex AI Plaorm (e.g., Model Garden, Vertex AI Search, AutoML). Recognizing the functionality, use cases, and business value of Google Cloud’s RAG oerings (e.g., prebuilt RAG with Vertex AI Search, RAG APIs). Recognizing the functionality, use cases, and business value of using Vertex AI Agent Builder to build custom agents. 2.5 Dene the purpose and types of tooling for gen AI agents. Considerations include: Identifying how agents use tools to interact with the external environment and achieve tasks (e.g., extensions, functions, data stores, and plugins). Identifying relevant Google Cloud services and pre-built AI APIs for agent tooling (e.g., Cloud Storage, databases, Cloud Functions, Cloud Run, Vertex AI, Speech-to-Text API, Text-to-Speech API, Translation API, Document Translation API, Document AI API, Cloud Vision API, Cloud Video Intelligence API, Natural Language API, Google Cloud API Library). Determining when to use Vertex AI Studio and Google AI Studio. |
| Techniques to improve gen AI model output | 20% | 3.1 Describe how to proactively overcome foundation model limitations. Considerations include: Identifying common limitations of foundation models (e.g., data dependency, the knowledge cuto, bias, fairness, hallucinations, edge cases). Describing the Google Cloud-recommended practices to address limitations (e.g., grounding, retrieval-augmented generation [RAG], prompt engineering, ne-tuning, human in the loop [HITL]). Recognizing Google-recommended practices for continuous monitoring and evaluation of gen AI models (e.g., automatic model upgrades, key performance indicators, security patches and updates, versioning, performance tracking, dri monitoring, Vertex AI Feature Store). 3.2 Describe prompt engineering techniques and how they drive beer results. Considerations include: Dening prompt engineering and describing its signicance in interacting with large language models (LLMs). Identifying prompting techniques and use cases (e.g., zero-shot, one-shot, few-shot, role prompting, prompt chaining). Identifying advanced prompting techniques and when to use them (e.g., chain-of-thought prompting, ReAct prompting). 3.3 Identify grounding techniques and their use cases. Considerations include: Describing the concept of grounding in LLMs and dierentiating between grounding with rst-party enterprise data, third-party data, and world data. Describing how retrieval-augmented generation (RAG) can aect the generated output from your gen AI models. Google Cloud grounding oerings: a. Pre-built RAG with Vertex AI Search b. RAG APIs c. Grounding with Google Search Identifying how sampling parameters and seings are used to control the behavior of gen AI models (e.g., token count, temperature, top-p [nucleus sampling], safety seings, and output length). |
| Business strategies for a successful gen AI solution | 15% | 4.1 Describe the Google Cloud-recommended steps to successfully implement a transformational gen AI solution. Considerations include: Recognizing the dierent types of gen AI solutions (e.g., text generation, image generation, code generation, personalized user needs). Identifying the key factors that inuence gen AI needs (e.g., business requirements, technical constraints). Describing how to choose the right gen AI solution for a specic business need. Identifying the steps to integrate gen AI into an organization. Identifying techniques to measure the impact of gen AI initiatives 4.2 Dene secure AI and its importance in protecting AI systems from malicious attacks and misuse. Considerations include: Explaining security throughout the ML lifecycle. Identifying the purpose and benets of Google’s Secure AI Framework (SAIF). Recognizing Google Cloud security tools and their purpose (e.g., secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring tools). 4.3 Describe the importance of responsible AI in business. Considerations include: Explaining the importance of responsible AI and transparency. Describing privacy considerations (e.g., privacy risks, data anonymization, and pseudonymization). Describing the implications of data quality, bias, and fairness. Describing the importance of accountability and explainability in AI systems. |
| Official Information | https://cloud.google.com/developers/generative-ai-leader |
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